Negotiating Privacy Constraints in Online Social Networks

Abstract

Privacy is a major concern of Web systems. Traditional Web systems employ static privacy agreements to notify its users of how their information will be used. Recent social networks allow users to specify some privacy concerns, thus providing a partially personalized privacy setting. However, still privacy violations are taking place because of different privacy concerns, based on context, audience, or content that cannot be enumerated by a user up front. Accordingly, we propose that privacy should be handled per post and on demand among all that might be affected. To realize this, we envision a multiagent system where each user in a social network is represented by an agent. When a user engages in an activity that could jeopardize a user’s privacy (e.g., publishing a picture), agents of the users negotiate on the privacy concerns that will govern the content. We employ a negotiation protocol and use it to settle differences in privacy expectations. We develop a novel agent that represents its user’s preferences semantically and reason on privacy concerns effectively. Execution of our agent on privacy scenarios from the literature show that our approach can handle and resolve realistic privacy violations before they occur.

This work has been supported by TUBITAK under grant 113E543.